Shoreline change is affected by a multitude of complex processes operating at various spatiotemporal scales. Comprehensive multi-year simulations of shoreline changes and forecasts are feasible with process-based models. However, these detailed and computationally expensive numerical simulations do not always lead to increased predictive skill in comparison to simpler shoreline models (Davidson et al., 2013). ShoreFor (Davidson et al., 2013) employs the concept of (dis-) equilibrium of shoreline location following Wright and Short (1985). In this research, the current ShoreFor model (Splinter et al., 2014) is used as baseline. ShoreFor seeks for an optimum decay factor that best describes the morphological response of a coastal system to the corresponding hydrodynamic forcing. This parameter is measured in days and effectively controls the shoreline response timescale. Currently, the ShoreFor model provides a single value for φ, representing a single dominant shoreline response timescale. As morphological systems can contain multiple dominant timescale responses, a new approach to multi-timescale shoreline change modelling is proposed. Three video-derived datasets are used to improve the model towards a generally applicable one which incorporates multiple temporal scales: Narrabeen (Australia), Nha Trang (Vietnam) and Grand Popo (Benin). Each dataset has different hydrodynamic- and morphological characteristics. The storm timescale is a dominant mode of shoreline response for Narrabeen, whereas for Nha Trang and Grand Popo the seasonal timescale is the most dominant. Furthermore, all sites are subjected to more modes of shoreline response such that the application of a single memory decay factor will hamper shoreline modelling. The existing model is improved using 3 steps.In the first step, the raw wave- and shoreline signals are filtered to distinguish temporal scales. Then filtered temporal scales in shoreline position are forced with the corresponding scales in the wave signals. For each temporal scale, a distinct memory decay factor φ is found. In the second step, the effect of small temporal scales in wave forcing on larger temporal scales in shoreline position is accounted for. The improved model takes this effect into account using the envelope of the filtered wave signals. The envelope is used to force the model and to calculate shoreline change with the same timescale. In the third and final step, the effect of large temporal scales in shoreline position on smaller scales in shoreline response is accounted for. The efficiency with which waves induce cross-shore sediment transport can be dependent on the large scale shoreline variation. A time varying response factor c is introduced that controls the efficiency with which waves induce cross-shore sediment transport. The dynamic response factor varies over time with the shape of the larger scale shoreline signal: it represents the effect of the large scale shoreline variation on the smaller scale shoreline response.